Architecture as Irony in Michael Ondaatje's Novel In the Skin of a Lion
Bibliographic record
Abstract
In this study I have analysed the different, often ironic, ways of representing well-known works of architecture in Michael Ondaatje’s novel In the Skin of a Lion (1987). The Bloor Street Viaduct and the waterworks, which are under construction in the narrative, are famous landmarks in the city of Toronto. I have studied how the different characters (from different social classes) refer to the works of architecture in the novel, what kind of language they use, and what are their attitudes towards the artefacts. According to Michel Foucault, architecture may be used as a disciplinary tool in society. Buildings can support the prevailing hierarchical structures both symbolically and practically. On the other hand, Linda Hutcheon states that the ironic interpretation contests single meanings. In this study, I have claimed that the ironic interpretation of the works of architecture enables the characters to cross the imaginary boundaries in society, that is, irony as a literary device in the novel offers a view to Canadian society as manifold and dynamic. \n\nThe conclusions of the study was that even though the edifices in the novel seem to function as a support of the disciplinary society, irony generates border-crossings, which mock the power hierarchies and gradually change the structures of society. Ironic representation in the novel, therefore, contests the notion of Canadian society as stagnant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".